Posted
by
Soulskill
on Wednesday February 06, 2013 @02:20AM
from the from-unclesam-import-money dept.

itwbennett writes "DARPA (the U.S. Defense Advanced Research Projects Agency) has awarded $3 million to software provider Continuum Analytics to help fund the development of Python's data processing and visualization capabilities for big data jobs. The money will go toward developing new techniques for data analysis and for visually portraying large, multi-dimensional data sets. The work aims to extend beyond the capabilities offered by the NumPy and SciPy Python libraries, which are widely used by programmers for mathematical and scientific calculations, respectively. The work is part of DARPA's XData research program, a four-year, $100 million effort to give the Defense Department and other U.S. government agencies tools to work with large amounts of sensor data and other forms of big data."

> I might get to learn Python one day but I'm afraid I'd become a so-so programmer in both languages.

I empathize since I conversely only barely use Ruby. Once someone learns one of these languages, there is not that much that the other offers. But happily, one need not learn advanced Python to benefit from these projects.

> it's a shame that so much effort is being divided between communities

AFAIK, all scientific funding from US and Europe is/was always directed to Python, not Ruby. So Python is firmly established as a research language and there is not much effort being divided with Ruby (which seems to have a much more spotted and amateur movement in this direction), at least as far as scientific stuff is concerned (Ruby is more popular on web app side). For me the tension for scientific use is not between Python and Ruby, but between Python and R. Python community is replicating a lot of R functionality these days but R still has a much better lead in science libraries. Happily, it is quite easy to call R from Python.

Compared to plain old Python, yes. But Cython offers a lot of capabilities that improve speed dramatically - just using a type for your data in Cython gives programs a wonderful boost in speed.

As someone who uses Matlab for most of my programming, I have come to detest languages that do not force specifying a variable type and/or declaring variables. Matlab offers neither, but it is a standard in some circles.

The GIL is an overblown issue. Threading is designed to get around issues with accessing slow resources, not for serious parallel computing. Just use multiprocessing if you want to do lots of computing in parallel, problem solved.